Gait Performance of Friction-Based Prosthetic Knee Joint Swing-Phase Controllers in Under-Resourced Settings
Bibliographic record
Abstract
Gait quality can influence walking ability and mobility outcomes making it an important part of prosthetic rehabilitation. Prosthetic knee joint designs can influence gait quality, and limited data exists to guide component selection in under-resourced settings. This study compared spatiotemporal and kinematic gait parameters for two common types of friction-based swing-phase controlled prosthetic knee joints. Two-dimensional optical gait analysis was conducted as part of a cross-over study design involving 17 individuals with unilateral transfemoral amputations. Two prosthetic knee joints were compared. One utilized constant-friction (CF) and the other a variable cadence controller (VCC) for swing-phase control. Gait was analyzed at normal and fast walking speeds. Primary gait parameters included swing-phase time, step length, and knee flexion. Swing-phase time and peak knee flexion angles, as well as their related symmetry indices, were lower for the VCC compared to the CF (p < 0.01), by 11.1 to 94.1%. The VCC resulted in faster walking speeds by approximately 15% compared to the CF (p = 0.002). Friction-based swing-phase knee control mechanisms can facilitate an appropriate and cost-effective prosthetic knee joint solution in under-resourced settings. The findings suggest that friction-based mechanism can be designed to improve gait quality, and in turn overall walking performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".